While recording neuroelectric potentials, we presented a set of acoustically vocoded consonants (aKa, aSHa, and aNa) to normal-hearing listeners (n = 12) that simulated speech tokens processed through four different combinations of CI stimulation rate and number of spectral maxima. Parameter settings were selected to feature relatively fast/slow stimulation rates and high/low number of maxima; 1800 pps/20 maxima, 1800/8, 500/20 and 500/8.

Results:

Speech identification and reaction times did not differ with changes in either the number of maxima or stimulation rate indicating ceiling behavioral performance. Similarly, we found that conventional univariate analysis (analysis of variance) of N1 and P2 amplitude/latency failed to reveal strong modulations across CI-processed speech conditions. In contrast, multivariate discriminant analysis based on a combination of neural measures was used to create “neural confusion matrices” and identified a unique parameter set (1800/8) that maximally differentiated speech tokens at the neural level. This finding was corroborated by information transfer analysis which confirmed these settings optimally transmitted information in listeners’ neural and perceptual responses.

Conclusions:

Translated to actual implant patients, our findings suggest that scalp-recorded ERPs might be useful in determining optimal signal processing settings from among a closed set of parameter options and aid in the objective fitting of CI devices.